As semiconductor manufacturing continues its march towards more advanced technology nodes, it becomes increasingly important to identify and characterize design weak points, which is typically done using a combination...
详细信息
ISBN:
(数字)9781510607422
ISBN:
(纸本)9781510607415;9781510607422
As semiconductor manufacturing continues its march towards more advanced technology nodes, it becomes increasingly important to identify and characterize design weak points, which is typically done using a combination of inline inspection data and the physical layout (or design). However, the employed methodologies have been somewhat imprecise, relying greatly on statistical techniques to signal excursions. Therefore, common operations such as background-based binning that are designed to identify frequently failing patterns cannot reliably identify specific weak patterns. They can only identify an approximate set of possible weak patterns, but within these sets there are many perfectly good patterns. Additionally, characterizing the failure rate of a known weak pattern based on inline inspection data also has a lot of fuzziness. SEM (Scanning Electron Microscope) Review attempts to come to the rescue by capturing high resolution images of the regions surrounding the reported defect locations, but SEM images are reviewed by human operators and the weak patterns revealed in those images must be manually identified and classified. Compounding the problem is the fact that a single Review SEM image may contain multiple defective patterns and several of those patterns might not appear defective to the human eye. In this paper we describe a significantly improved methodology that brings advanced computer image processing and design-overlay techniques to better address the challenges posed by today's leading technology nodes. Specifically, new software techniques allow the computer to analyze Review SEM images in detail, to overlay those images with reference design to detect every defect that might be present in all regions of interest within the overlaid reference design (including several classes of defects that human operators will typically miss), to obtain the exact defect location on design, to compare all defective patterns thus detected against a library of known
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